├── .gitignore ├── resources ├── build.png └── cover.jpg ├── .gitattributes ├── models ├── yolov4.weights ├── yolov4-tiny.weights ├── coco.names ├── yolov4-tiny.cfg └── yolov4.cfg ├── dnn_inference.py ├── README.md └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | *.mp4 -------------------------------------------------------------------------------- /resources/build.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kingardor/YOLOv4-OpenCV-CUDA-DNN/HEAD/resources/build.png -------------------------------------------------------------------------------- /resources/cover.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kingardor/YOLOv4-OpenCV-CUDA-DNN/HEAD/resources/cover.jpg -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | models/yolov4-tiny.weights filter=lfs diff=lfs merge=lfs -text 2 | models/yolov4.weights filter=lfs diff=lfs merge=lfs -text 3 | -------------------------------------------------------------------------------- /models/yolov4.weights: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:8463fde6ee7130a947a73104ce73c6fa88618a9d9ecd4a65d0b38f07e17ec4e4 3 | size 257717640 4 | -------------------------------------------------------------------------------- /models/yolov4-tiny.weights: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:cf9fbfd0f6d4869b35762f56100f50ed05268084078805f0e7989efe5bb8ca87 3 | size 24251276 4 | -------------------------------------------------------------------------------- /models/coco.names: -------------------------------------------------------------------------------- 1 | person 2 | bicycle 3 | car 4 | motorbike 5 | aeroplane 6 | bus 7 | train 8 | truck 9 | boat 10 | traffic light 11 | fire hydrant 12 | stop sign 13 | parking meter 14 | bench 15 | bird 16 | cat 17 | dog 18 | horse 19 | sheep 20 | cow 21 | elephant 22 | bear 23 | zebra 24 | giraffe 25 | backpack 26 | umbrella 27 | handbag 28 | tie 29 | suitcase 30 | frisbee 31 | skis 32 | snowboard 33 | sports ball 34 | kite 35 | baseball bat 36 | baseball glove 37 | skateboard 38 | surfboard 39 | tennis racket 40 | bottle 41 | wine glass 42 | cup 43 | fork 44 | knife 45 | spoon 46 | bowl 47 | banana 48 | apple 49 | sandwich 50 | orange 51 | broccoli 52 | carrot 53 | hot dog 54 | pizza 55 | donut 56 | cake 57 | chair 58 | sofa 59 | pottedplant 60 | bed 61 | diningtable 62 | toilet 63 | tvmonitor 64 | laptop 65 | mouse 66 | remote 67 | keyboard 68 | cell phone 69 | microwave 70 | oven 71 | toaster 72 | sink 73 | refrigerator 74 | book 75 | clock 76 | vase 77 | scissors 78 | teddy bear 79 | hair drier 80 | toothbrush 81 | -------------------------------------------------------------------------------- /models/yolov4-tiny.cfg: -------------------------------------------------------------------------------- 1 | [net] 2 | # Testing 3 | #batch=1 4 | #subdivisions=1 5 | # Training 6 | batch=64 7 | subdivisions=1 8 | width=416 9 | height=416 10 | channels=3 11 | momentum=0.9 12 | decay=0.0005 13 | angle=0 14 | saturation = 1.5 15 | exposure = 1.5 16 | hue=.1 17 | 18 | learning_rate=0.00261 19 | burn_in=1000 20 | max_batches = 500200 21 | policy=steps 22 | steps=400000,450000 23 | scales=.1,.1 24 | 25 | [convolutional] 26 | batch_normalize=1 27 | filters=32 28 | size=3 29 | stride=2 30 | pad=1 31 | activation=leaky 32 | 33 | [convolutional] 34 | batch_normalize=1 35 | filters=64 36 | size=3 37 | stride=2 38 | pad=1 39 | activation=leaky 40 | 41 | [convolutional] 42 | batch_normalize=1 43 | filters=64 44 | size=3 45 | stride=1 46 | pad=1 47 | activation=leaky 48 | 49 | [route] 50 | layers=-1 51 | groups=2 52 | group_id=1 53 | 54 | [convolutional] 55 | batch_normalize=1 56 | filters=32 57 | size=3 58 | stride=1 59 | pad=1 60 | activation=leaky 61 | 62 | [convolutional] 63 | batch_normalize=1 64 | filters=32 65 | size=3 66 | stride=1 67 | pad=1 68 | activation=leaky 69 | 70 | [route] 71 | layers = -1,-2 72 | 73 | [convolutional] 74 | batch_normalize=1 75 | filters=64 76 | size=1 77 | stride=1 78 | pad=1 79 | activation=leaky 80 | 81 | [route] 82 | layers = -6,-1 83 | 84 | [maxpool] 85 | size=2 86 | stride=2 87 | 88 | [convolutional] 89 | batch_normalize=1 90 | filters=128 91 | size=3 92 | stride=1 93 | pad=1 94 | activation=leaky 95 | 96 | [route] 97 | layers=-1 98 | groups=2 99 | group_id=1 100 | 101 | [convolutional] 102 | batch_normalize=1 103 | filters=64 104 | size=3 105 | stride=1 106 | pad=1 107 | activation=leaky 108 | 109 | [convolutional] 110 | batch_normalize=1 111 | filters=64 112 | size=3 113 | stride=1 114 | pad=1 115 | activation=leaky 116 | 117 | [route] 118 | layers = -1,-2 119 | 120 | [convolutional] 121 | batch_normalize=1 122 | filters=128 123 | size=1 124 | stride=1 125 | pad=1 126 | activation=leaky 127 | 128 | [route] 129 | layers = -6,-1 130 | 131 | [maxpool] 132 | size=2 133 | stride=2 134 | 135 | [convolutional] 136 | batch_normalize=1 137 | filters=256 138 | size=3 139 | stride=1 140 | pad=1 141 | activation=leaky 142 | 143 | [route] 144 | layers=-1 145 | groups=2 146 | group_id=1 147 | 148 | [convolutional] 149 | batch_normalize=1 150 | filters=128 151 | size=3 152 | stride=1 153 | pad=1 154 | activation=leaky 155 | 156 | [convolutional] 157 | batch_normalize=1 158 | filters=128 159 | size=3 160 | stride=1 161 | pad=1 162 | activation=leaky 163 | 164 | [route] 165 | layers = -1,-2 166 | 167 | [convolutional] 168 | batch_normalize=1 169 | filters=256 170 | size=1 171 | stride=1 172 | pad=1 173 | activation=leaky 174 | 175 | [route] 176 | layers = -6,-1 177 | 178 | [maxpool] 179 | size=2 180 | stride=2 181 | 182 | [convolutional] 183 | batch_normalize=1 184 | filters=512 185 | size=3 186 | stride=1 187 | pad=1 188 | activation=leaky 189 | 190 | ################################## 191 | 192 | [convolutional] 193 | batch_normalize=1 194 | filters=256 195 | size=1 196 | stride=1 197 | pad=1 198 | activation=leaky 199 | 200 | [convolutional] 201 | batch_normalize=1 202 | filters=512 203 | size=3 204 | stride=1 205 | pad=1 206 | activation=leaky 207 | 208 | [convolutional] 209 | size=1 210 | stride=1 211 | pad=1 212 | filters=255 213 | activation=linear 214 | 215 | 216 | 217 | [yolo] 218 | mask = 3,4,5 219 | anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 220 | classes=80 221 | num=6 222 | jitter=.3 223 | scale_x_y = 1.05 224 | cls_normalizer=1.0 225 | iou_normalizer=0.07 226 | iou_loss=ciou 227 | ignore_thresh = .7 228 | truth_thresh = 1 229 | random=0 230 | resize=1.5 231 | nms_kind=greedynms 232 | beta_nms=0.6 233 | 234 | [route] 235 | layers = -4 236 | 237 | [convolutional] 238 | batch_normalize=1 239 | filters=128 240 | size=1 241 | stride=1 242 | pad=1 243 | activation=leaky 244 | 245 | [upsample] 246 | stride=2 247 | 248 | [route] 249 | layers = -1, 23 250 | 251 | [convolutional] 252 | batch_normalize=1 253 | filters=256 254 | size=3 255 | stride=1 256 | pad=1 257 | activation=leaky 258 | 259 | [convolutional] 260 | size=1 261 | stride=1 262 | pad=1 263 | filters=255 264 | activation=linear 265 | 266 | [yolo] 267 | mask = 1,2,3 268 | anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 269 | classes=80 270 | num=6 271 | jitter=.3 272 | scale_x_y = 1.05 273 | cls_normalizer=1.0 274 | iou_normalizer=0.07 275 | iou_loss=ciou 276 | ignore_thresh = .7 277 | truth_thresh = 1 278 | random=0 279 | resize=1.5 280 | nms_kind=greedynms 281 | beta_nms=0.6 282 | -------------------------------------------------------------------------------- /dnn_inference.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import cv2 3 | import argparse 4 | import random 5 | import time 6 | 7 | class YOLOv4: 8 | 9 | def __init__(self): 10 | """ Method called when object of this class is created. """ 11 | 12 | self.args = None 13 | self.net = None 14 | self.names = None 15 | 16 | self.parse_arguments() 17 | self.initialize_network() 18 | self.run_inference() 19 | 20 | def parse_arguments(self): 21 | """ Method to parse arguments using argparser. """ 22 | 23 | parser = argparse.ArgumentParser(description='Object Detection using YOLOv4 and OpenCV4') 24 | parser.add_argument('--image', type=str, default='', help='Path to use images') 25 | parser.add_argument('--stream', type=str, default='', help='Path to use video stream') 26 | parser.add_argument('--cfg', type=str, default='models/yolov4.cfg', help='Path to cfg to use') 27 | parser.add_argument('--weights', type=str, default='models/yolov4.weights', help='Path to weights to use') 28 | parser.add_argument('--namesfile', type=str, default='models/coco.names', help='Path to names to use') 29 | parser.add_argument('--input_size', type=int, default=416, help='Input size') 30 | parser.add_argument('--use_gpu', default=False, action='store_true', help='To use NVIDIA GPU or not') 31 | 32 | self.args = parser.parse_args() 33 | 34 | def initialize_network(self): 35 | """ Method to initialize and load the model. """ 36 | 37 | self.net = cv2.dnn_DetectionModel(self.args.cfg, self.args.weights) 38 | 39 | if self.args.use_gpu: 40 | self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA) 41 | self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA) 42 | else: 43 | self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV) 44 | self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU) 45 | 46 | if not self.args.input_size % 32 == 0: 47 | print('[Error] Invalid input size! Make sure it is a multiple of 32. Exiting..') 48 | sys.exit(0) 49 | self.net.setInputSize(self.args.input_size, self.args.input_size) 50 | self.net.setInputScale(1.0 / 255) 51 | self.net.setInputSwapRB(True) 52 | with open(self.args.namesfile, 'rt') as f: 53 | self.names = f.read().rstrip('\n').split('\n') 54 | 55 | def image_inf(self): 56 | """ Method to run inference on image. """ 57 | 58 | frame = cv2.imread(self.args.image) 59 | 60 | timer = time.time() 61 | classes, confidences, boxes = self.net.detect(frame, confThreshold=0.1, nmsThreshold=0.4) 62 | print('[Info] Time Taken: {}'.format(time.time() - timer), end='\r') 63 | 64 | if(not len(classes) == 0): 65 | for classId, confidence, box in zip(classes.flatten(), confidences.flatten(), boxes): 66 | label = '%s: %.2f' % (self.names[classId], confidence) 67 | left, top, width, height = box 68 | b = random.randint(0, 255) 69 | g = random.randint(0, 255) 70 | r = random.randint(0, 255) 71 | cv2.rectangle(frame, box, color=(b, g, r), thickness=2) 72 | cv2.rectangle(frame, (left, top), (left + len(label) * 20, top - 30), (b, g, r), cv2.FILLED) 73 | cv2.putText(frame, label, (left, top), cv2.FONT_HERSHEY_COMPLEX, 1, (255 - b, 255 - g, 255 - r), 1, cv2.LINE_AA) 74 | 75 | cv2.imwrite('result.jpg', frame) 76 | cv2.imshow('Inference', frame) 77 | if cv2.waitKey(0) & 0xFF == ord('q'): 78 | return 79 | 80 | def stream_inf(self): 81 | """ Method to run inference on a stream. """ 82 | 83 | source = cv2.VideoCapture(0 if self.args.stream == 'webcam' else self.args.stream) 84 | 85 | b = random.randint(0, 255) 86 | g = random.randint(0, 255) 87 | r = random.randint(0, 255) 88 | 89 | while(source.isOpened()): 90 | ret, frame = source.read() 91 | if ret: 92 | timer = time.time() 93 | classes, confidences, boxes = self.net.detect(frame, confThreshold=0.1, nmsThreshold=0.4) 94 | print('[Info] Time Taken: {} | FPS: {}'.format(time.time() - timer, 1/(time.time() - timer)), end='\r') 95 | 96 | if(not len(classes) == 0): 97 | for classId, confidence, box in zip(classes.flatten(), confidences.flatten(), boxes): 98 | label = '%s: %.2f' % (self.names[classId], confidence) 99 | left, top, width, height = box 100 | b = random.randint(0, 255) 101 | g = random.randint(0, 255) 102 | r = random.randint(0, 255) 103 | cv2.rectangle(frame, box, color=(b, g, r), thickness=2) 104 | cv2.rectangle(frame, (left, top), (left + len(label) * 20, top - 30), (b, g, r), cv2.FILLED) 105 | cv2.putText(frame, label, (left, top), cv2.FONT_HERSHEY_COMPLEX, 1, (255 - b, 255 - g, 255 - r), 1, cv2.LINE_AA) 106 | 107 | cv2.imshow('Inference', frame) 108 | if cv2.waitKey(1) & 0xFF == ord('q'): 109 | break 110 | 111 | def run_inference(self): 112 | 113 | if self.args.image == '' and self.args.stream == '': 114 | print('[Error] Please provide a valid path for --image or --stream.') 115 | sys.exit(0) 116 | 117 | if not self.args.image == '': 118 | self.image_inf() 119 | 120 | elif not self.args.stream == '': 121 | self.stream_inf() 122 | 123 | cv2.destroyAllWindows() 124 | 125 | 126 | 127 | if __name__== '__main__': 128 | 129 | yolo = YOLOv4.__new__(YOLOv4) 130 | yolo.__init__() 131 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # YOLOv4 OpenCV CUDA DNN 2 | 3 | Run YOLOv4 directly with OpenCV using the CUDA enabled DNN module. 4 | 5 | ![Photo by Christopher Burns on Unsplash](resources/cover.jpg) 6 | 7 | ## Index 8 | 9 | - [YOLOv4 OpenCV CUDA DNN](#yolov4-opencv-cuda-dnn) 10 | - [Index](#index) 11 | - [Clone the repository](#clone-the-repository) 12 | - [Building OpenCV 4.5.1 with CUDA 11.2 and GStreamer](#building-opencv-451-with-cuda-112-and-gstreamer) 13 | - [Running the code](#running-the-code) 14 | - [CPU vs. GPU Performance Metrics](#cpu-vs-gpu-performance-metrics) 15 | - [Citations](#citations) 16 | 17 | ## Clone the repository 18 | 19 | This is a straightforward step, however, if you are new to git or git-lfs, I recommend glancing threw the steps. 20 | 21 | First, install git and git-lfs 22 | 23 | ```sh 24 | sudo apt install git git-lfs 25 | ``` 26 | 27 | Next, clone the repository 28 | 29 | ```sh 30 | # Using HTTPS 31 | git clone https://github.com/aj-ames/YOLOv4-OpenCV-DNN.git 32 | # Using SSH 33 | git clone git@github.com:aj-ames/YOLOv4-OpenCV-DNN.git 34 | ``` 35 | 36 | Finally, enable lfs and pull the yolo weights 37 | 38 | ```sh 39 | git lfs install 40 | git lfs pull 41 | ``` 42 | 43 | ## Building OpenCV 4.5.1 with CUDA 11.2 and GStreamer 44 | 45 | Make sure you have a working build of python3.7/3.8, CUDA and cuDNN. 46 | 47 | To install CUDA and cuDNN, use the following links - 48 | 49 | https://developer.nvidia.com/cuda-downloads 50 | https://developer.nvidia.com/rdp/cudnn-download 51 | 52 | ```sh 53 | sudo apt install python3-dev python3-pip python3-testresources 54 | ``` 55 | 56 | The dependencies needed are the following: 57 | 58 | ```sh 59 | sudo apt install build-essential cmake pkg-config unzip yasm git checkinstall 60 | sudo apt install libjpeg-dev libpng-dev libtiff-dev 61 | sudo apt install libavcodec-dev libavformat-dev libswscale-dev libavresample-dev 62 | sudo apt install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev 63 | sudo apt install libxvidcore-dev x264 libx264-dev libfaac-dev libmp3lame-dev libtheora-dev 64 | sudo apt install libfaac-dev libmp3lame-dev libvorbis-dev 65 | sudo apt install libopencore-amrnb-dev libopencore-amrwb-dev 66 | sudo apt-get install libgtk-3-dev 67 | sudo apt-get install libtbb-dev 68 | sudo apt-get install libatlas-base-dev gfortran 69 | sudo apt-get install libprotobuf-dev protobuf-compiler 70 | sudo apt-get install libgoogle-glog-dev libgflags-dev 71 | sudo apt-get install libgphoto2-dev libeigen3-dev libhdf5-dev doxygen 72 | ``` 73 | 74 | Install numpy 75 | 76 | ```sh 77 | pip3 install numpy 78 | ``` 79 | 80 | Now we move on to the source of OpenCV 81 | 82 | ```sh 83 | mkdir opencvbuild && cd opencvbuild 84 | wget -O opencv.zip https://github.com/opencv/opencv/archive/4.5.1.zip 85 | wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/4.5.1.zip 86 | unzip opencv.zip 87 | unzip opencv_contrib.zip 88 | mv opencv-4.5.1 opencv 89 | mv opencv_contrib-4.5.1 opencv_contrib 90 | ``` 91 | 92 | Once downloaded and extracted, you need to build and install it 93 | 94 | ```sh 95 | cd opencv 96 | mkdir build && cd build 97 | ``` 98 | 99 | Change the `CUDA_ARCH_BIN` value based on your GPU 100 | 101 | ```sh 102 | cmake \ 103 | -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_C_COMPILER=/usr/bin/gcc-7 \ 104 | -D CMAKE_INSTALL_PREFIX=/usr/local -D INSTALL_PYTHON_EXAMPLES=ON \ 105 | -D INSTALL_C_EXAMPLES=ON -D WITH_TBB=ON -D WITH_CUDA=ON -D WITH_CUDNN=ON \ 106 | -D OPENCV_DNN_CUDA=ON -D CUDA_ARCH_BIN=7.5 -D BUILD_opencv_cudacodec=OFF \ 107 | -D ENABLE_FAST_MATH=1 -D CUDA_FAST_MATH=1 -D WITH_CUBLAS=1 \ 108 | -D WITH_V4L=ON -D WITH_QT=OFF -D WITH_OPENGL=ON -D WITH_GSTREAMER=ON \ 109 | -D WITH_FFMPEG=ON -D OPENCV_GENERATE_PKGCONFIG=ON \ 110 | -D OPENCV_PC_FILE_NAME=opencv4.pc -D OPENCV_ENABLE_NONFREE=ON \ 111 | -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \ 112 | -D PYTHON_DEFAULT_EXECUTABLE=$(which python3) -D BUILD_EXAMPLES=ON .. 113 | ``` 114 | 115 | Your configuration should look something like this - 116 | 117 | ![Build Image](resources/build.png) 118 | 119 | Once done, go ahead and complete the build and install 120 | 121 | ```sh 122 | make -j$(nproc) 123 | sudo make install 124 | ``` 125 | 126 | ## Running the code 127 | 128 | The code supports a number of command line arguments. Use help to see all supported arguments 129 | 130 | ```sh 131 | ➜ python3 dnn_inference.py --help 132 | usage: dnn_inference.py [-h] [--image IMAGE] [--stream STREAM] [--cfg CFG] 133 | [--weights WEIGHTS] [--namesfile NAMESFILE] 134 | [--input_size INPUT_SIZE] [--use_gpu] 135 | 136 | Object Detection using YOLOv4 and OpenCV4 137 | 138 | optional arguments: 139 | -h, --help show this help message and exit 140 | --image IMAGE Path to use images 141 | --stream STREAM Path to use video stream 142 | --cfg CFG Path to cfg to use 143 | --weights WEIGHTS Path to weights to use 144 | --namesfile NAMESFILE 145 | Path to names to use 146 | --input_size INPUT_SIZE 147 | Input size 148 | --use_gpu To use NVIDIA GPU or not 149 | ``` 150 | 151 | To pass an image, run the script in the following way: 152 | 153 | ```sh 154 | python3 dnn_infernece.py --image images/example.jpg --use_gpu 155 | ``` 156 | 157 | To run a stream, run the script this way: 158 | 159 | ```sh 160 | # Video 161 | python3 dnn_inference.py --stream video.mp4 --use_gpu 162 | 163 | # RTSP 164 | python3 dnn_inference.py --stream rtsp://192.168.1.1:554/stream --use_gpu 165 | 166 | # Webcam 167 | python3 dnn_inference.py --stream webcam --use_gpu 168 | ``` 169 | 170 | ## CPU vs. GPU Performance Metrics 171 | 172 | I have tested on two configurations 173 | 174 | 1. Intel Core i5 7300HQ + NVIDIA GeForce GTX 1050Ti 175 | 2. Intel Xeon E5-1650 v4 + NVIDIA Tesla T4 176 | 177 | | Device | FPS | Device | FPS | 178 | | :------------- | :----------: | :------------- | :----------: | 179 | | Core i5 7300HQ | 2.1 | GTX 1050 Ti | 20.1 | 180 | | Xeon E5-1650 | 3.5 | Tesla T4 | 42.3 | 181 | 182 | ## Citations 183 | 184 | https://github.com/AlexeyAB/darknet 185 | -------------------------------------------------------------------------------- /models/yolov4.cfg: -------------------------------------------------------------------------------- 1 | [net] 2 | # Testing 3 | #batch=1 4 | #subdivisions=1 5 | # Training 6 | batch=64 7 | subdivisions=8 8 | width=608 9 | height=608 10 | channels=3 11 | momentum=0.949 12 | decay=0.0005 13 | angle=0 14 | saturation = 1.5 15 | exposure = 1.5 16 | hue=.1 17 | 18 | learning_rate=0.00261 19 | burn_in=1000 20 | max_batches = 500500 21 | policy=steps 22 | steps=400000,450000 23 | scales=.1,.1 24 | 25 | #cutmix=1 26 | mosaic=1 27 | 28 | #:104x104 54:52x52 85:26x26 104:13x13 for 416 29 | 30 | [convolutional] 31 | batch_normalize=1 32 | filters=32 33 | size=3 34 | stride=1 35 | pad=1 36 | activation=mish 37 | 38 | # Downsample 39 | 40 | [convolutional] 41 | batch_normalize=1 42 | filters=64 43 | size=3 44 | stride=2 45 | pad=1 46 | activation=mish 47 | 48 | [convolutional] 49 | batch_normalize=1 50 | filters=64 51 | size=1 52 | stride=1 53 | pad=1 54 | activation=mish 55 | 56 | [route] 57 | layers = -2 58 | 59 | [convolutional] 60 | batch_normalize=1 61 | filters=64 62 | size=1 63 | stride=1 64 | pad=1 65 | activation=mish 66 | 67 | [convolutional] 68 | batch_normalize=1 69 | filters=32 70 | size=1 71 | stride=1 72 | pad=1 73 | activation=mish 74 | 75 | [convolutional] 76 | batch_normalize=1 77 | filters=64 78 | size=3 79 | stride=1 80 | pad=1 81 | activation=mish 82 | 83 | [shortcut] 84 | from=-3 85 | activation=linear 86 | 87 | [convolutional] 88 | batch_normalize=1 89 | filters=64 90 | size=1 91 | stride=1 92 | pad=1 93 | activation=mish 94 | 95 | [route] 96 | layers = -1,-7 97 | 98 | [convolutional] 99 | batch_normalize=1 100 | filters=64 101 | size=1 102 | stride=1 103 | pad=1 104 | activation=mish 105 | 106 | # Downsample 107 | 108 | [convolutional] 109 | batch_normalize=1 110 | filters=128 111 | size=3 112 | stride=2 113 | pad=1 114 | activation=mish 115 | 116 | [convolutional] 117 | batch_normalize=1 118 | filters=64 119 | size=1 120 | stride=1 121 | pad=1 122 | activation=mish 123 | 124 | [route] 125 | layers = -2 126 | 127 | [convolutional] 128 | batch_normalize=1 129 | filters=64 130 | size=1 131 | stride=1 132 | pad=1 133 | activation=mish 134 | 135 | [convolutional] 136 | batch_normalize=1 137 | filters=64 138 | size=1 139 | stride=1 140 | pad=1 141 | activation=mish 142 | 143 | [convolutional] 144 | batch_normalize=1 145 | filters=64 146 | size=3 147 | stride=1 148 | pad=1 149 | activation=mish 150 | 151 | [shortcut] 152 | from=-3 153 | activation=linear 154 | 155 | [convolutional] 156 | batch_normalize=1 157 | filters=64 158 | size=1 159 | stride=1 160 | pad=1 161 | activation=mish 162 | 163 | [convolutional] 164 | batch_normalize=1 165 | filters=64 166 | size=3 167 | stride=1 168 | pad=1 169 | activation=mish 170 | 171 | [shortcut] 172 | from=-3 173 | activation=linear 174 | 175 | [convolutional] 176 | batch_normalize=1 177 | filters=64 178 | size=1 179 | stride=1 180 | pad=1 181 | activation=mish 182 | 183 | [route] 184 | layers = -1,-10 185 | 186 | [convolutional] 187 | batch_normalize=1 188 | filters=128 189 | size=1 190 | stride=1 191 | pad=1 192 | activation=mish 193 | 194 | # Downsample 195 | 196 | [convolutional] 197 | batch_normalize=1 198 | filters=256 199 | size=3 200 | stride=2 201 | pad=1 202 | activation=mish 203 | 204 | [convolutional] 205 | batch_normalize=1 206 | filters=128 207 | size=1 208 | stride=1 209 | pad=1 210 | activation=mish 211 | 212 | [route] 213 | layers = -2 214 | 215 | [convolutional] 216 | batch_normalize=1 217 | filters=128 218 | size=1 219 | stride=1 220 | pad=1 221 | activation=mish 222 | 223 | [convolutional] 224 | batch_normalize=1 225 | filters=128 226 | size=1 227 | stride=1 228 | pad=1 229 | activation=mish 230 | 231 | [convolutional] 232 | batch_normalize=1 233 | filters=128 234 | size=3 235 | stride=1 236 | pad=1 237 | activation=mish 238 | 239 | [shortcut] 240 | from=-3 241 | activation=linear 242 | 243 | [convolutional] 244 | batch_normalize=1 245 | filters=128 246 | size=1 247 | stride=1 248 | pad=1 249 | activation=mish 250 | 251 | [convolutional] 252 | batch_normalize=1 253 | filters=128 254 | size=3 255 | stride=1 256 | pad=1 257 | activation=mish 258 | 259 | [shortcut] 260 | from=-3 261 | activation=linear 262 | 263 | [convolutional] 264 | batch_normalize=1 265 | filters=128 266 | size=1 267 | stride=1 268 | pad=1 269 | activation=mish 270 | 271 | [convolutional] 272 | batch_normalize=1 273 | filters=128 274 | size=3 275 | stride=1 276 | pad=1 277 | activation=mish 278 | 279 | [shortcut] 280 | from=-3 281 | activation=linear 282 | 283 | [convolutional] 284 | batch_normalize=1 285 | filters=128 286 | size=1 287 | stride=1 288 | pad=1 289 | activation=mish 290 | 291 | [convolutional] 292 | batch_normalize=1 293 | filters=128 294 | size=3 295 | stride=1 296 | pad=1 297 | activation=mish 298 | 299 | [shortcut] 300 | from=-3 301 | activation=linear 302 | 303 | 304 | [convolutional] 305 | batch_normalize=1 306 | filters=128 307 | size=1 308 | stride=1 309 | pad=1 310 | activation=mish 311 | 312 | [convolutional] 313 | batch_normalize=1 314 | filters=128 315 | size=3 316 | stride=1 317 | pad=1 318 | activation=mish 319 | 320 | [shortcut] 321 | from=-3 322 | activation=linear 323 | 324 | [convolutional] 325 | batch_normalize=1 326 | filters=128 327 | size=1 328 | stride=1 329 | pad=1 330 | activation=mish 331 | 332 | [convolutional] 333 | batch_normalize=1 334 | filters=128 335 | size=3 336 | stride=1 337 | pad=1 338 | activation=mish 339 | 340 | [shortcut] 341 | from=-3 342 | activation=linear 343 | 344 | [convolutional] 345 | batch_normalize=1 346 | filters=128 347 | size=1 348 | stride=1 349 | pad=1 350 | activation=mish 351 | 352 | [convolutional] 353 | batch_normalize=1 354 | filters=128 355 | size=3 356 | stride=1 357 | pad=1 358 | activation=mish 359 | 360 | [shortcut] 361 | from=-3 362 | activation=linear 363 | 364 | [convolutional] 365 | batch_normalize=1 366 | filters=128 367 | size=1 368 | stride=1 369 | pad=1 370 | activation=mish 371 | 372 | [convolutional] 373 | batch_normalize=1 374 | filters=128 375 | size=3 376 | stride=1 377 | pad=1 378 | activation=mish 379 | 380 | [shortcut] 381 | from=-3 382 | activation=linear 383 | 384 | [convolutional] 385 | batch_normalize=1 386 | filters=128 387 | size=1 388 | stride=1 389 | pad=1 390 | activation=mish 391 | 392 | [route] 393 | layers = -1,-28 394 | 395 | [convolutional] 396 | batch_normalize=1 397 | filters=256 398 | size=1 399 | stride=1 400 | pad=1 401 | activation=mish 402 | 403 | # Downsample 404 | 405 | [convolutional] 406 | batch_normalize=1 407 | filters=512 408 | size=3 409 | stride=2 410 | pad=1 411 | activation=mish 412 | 413 | [convolutional] 414 | batch_normalize=1 415 | filters=256 416 | size=1 417 | stride=1 418 | pad=1 419 | activation=mish 420 | 421 | [route] 422 | layers = -2 423 | 424 | [convolutional] 425 | batch_normalize=1 426 | filters=256 427 | size=1 428 | stride=1 429 | pad=1 430 | activation=mish 431 | 432 | [convolutional] 433 | batch_normalize=1 434 | filters=256 435 | size=1 436 | stride=1 437 | pad=1 438 | activation=mish 439 | 440 | [convolutional] 441 | batch_normalize=1 442 | filters=256 443 | size=3 444 | stride=1 445 | pad=1 446 | activation=mish 447 | 448 | [shortcut] 449 | from=-3 450 | activation=linear 451 | 452 | 453 | [convolutional] 454 | batch_normalize=1 455 | filters=256 456 | size=1 457 | stride=1 458 | pad=1 459 | activation=mish 460 | 461 | [convolutional] 462 | batch_normalize=1 463 | filters=256 464 | size=3 465 | stride=1 466 | pad=1 467 | activation=mish 468 | 469 | [shortcut] 470 | from=-3 471 | activation=linear 472 | 473 | 474 | [convolutional] 475 | batch_normalize=1 476 | filters=256 477 | size=1 478 | stride=1 479 | pad=1 480 | activation=mish 481 | 482 | [convolutional] 483 | batch_normalize=1 484 | filters=256 485 | size=3 486 | stride=1 487 | pad=1 488 | activation=mish 489 | 490 | [shortcut] 491 | from=-3 492 | activation=linear 493 | 494 | 495 | [convolutional] 496 | batch_normalize=1 497 | filters=256 498 | size=1 499 | stride=1 500 | pad=1 501 | activation=mish 502 | 503 | [convolutional] 504 | batch_normalize=1 505 | filters=256 506 | size=3 507 | stride=1 508 | pad=1 509 | activation=mish 510 | 511 | [shortcut] 512 | from=-3 513 | activation=linear 514 | 515 | 516 | [convolutional] 517 | batch_normalize=1 518 | filters=256 519 | size=1 520 | stride=1 521 | pad=1 522 | activation=mish 523 | 524 | [convolutional] 525 | batch_normalize=1 526 | filters=256 527 | size=3 528 | stride=1 529 | pad=1 530 | activation=mish 531 | 532 | [shortcut] 533 | from=-3 534 | activation=linear 535 | 536 | 537 | [convolutional] 538 | batch_normalize=1 539 | filters=256 540 | size=1 541 | stride=1 542 | pad=1 543 | activation=mish 544 | 545 | [convolutional] 546 | batch_normalize=1 547 | filters=256 548 | size=3 549 | stride=1 550 | pad=1 551 | activation=mish 552 | 553 | [shortcut] 554 | from=-3 555 | activation=linear 556 | 557 | 558 | [convolutional] 559 | batch_normalize=1 560 | filters=256 561 | size=1 562 | stride=1 563 | pad=1 564 | activation=mish 565 | 566 | [convolutional] 567 | batch_normalize=1 568 | filters=256 569 | size=3 570 | stride=1 571 | pad=1 572 | activation=mish 573 | 574 | [shortcut] 575 | from=-3 576 | activation=linear 577 | 578 | [convolutional] 579 | batch_normalize=1 580 | filters=256 581 | size=1 582 | stride=1 583 | pad=1 584 | activation=mish 585 | 586 | [convolutional] 587 | batch_normalize=1 588 | filters=256 589 | size=3 590 | stride=1 591 | pad=1 592 | activation=mish 593 | 594 | [shortcut] 595 | from=-3 596 | activation=linear 597 | 598 | [convolutional] 599 | batch_normalize=1 600 | filters=256 601 | size=1 602 | stride=1 603 | pad=1 604 | activation=mish 605 | 606 | [route] 607 | layers = -1,-28 608 | 609 | [convolutional] 610 | batch_normalize=1 611 | filters=512 612 | size=1 613 | stride=1 614 | pad=1 615 | activation=mish 616 | 617 | # Downsample 618 | 619 | [convolutional] 620 | batch_normalize=1 621 | filters=1024 622 | size=3 623 | stride=2 624 | pad=1 625 | activation=mish 626 | 627 | [convolutional] 628 | batch_normalize=1 629 | filters=512 630 | size=1 631 | stride=1 632 | pad=1 633 | activation=mish 634 | 635 | [route] 636 | layers = -2 637 | 638 | [convolutional] 639 | batch_normalize=1 640 | filters=512 641 | size=1 642 | stride=1 643 | pad=1 644 | activation=mish 645 | 646 | [convolutional] 647 | batch_normalize=1 648 | filters=512 649 | size=1 650 | stride=1 651 | pad=1 652 | activation=mish 653 | 654 | [convolutional] 655 | batch_normalize=1 656 | filters=512 657 | size=3 658 | stride=1 659 | pad=1 660 | activation=mish 661 | 662 | [shortcut] 663 | from=-3 664 | activation=linear 665 | 666 | [convolutional] 667 | batch_normalize=1 668 | filters=512 669 | size=1 670 | stride=1 671 | pad=1 672 | activation=mish 673 | 674 | [convolutional] 675 | batch_normalize=1 676 | filters=512 677 | size=3 678 | stride=1 679 | pad=1 680 | activation=mish 681 | 682 | [shortcut] 683 | from=-3 684 | activation=linear 685 | 686 | [convolutional] 687 | batch_normalize=1 688 | filters=512 689 | size=1 690 | stride=1 691 | pad=1 692 | activation=mish 693 | 694 | [convolutional] 695 | batch_normalize=1 696 | filters=512 697 | size=3 698 | stride=1 699 | pad=1 700 | activation=mish 701 | 702 | [shortcut] 703 | from=-3 704 | activation=linear 705 | 706 | [convolutional] 707 | batch_normalize=1 708 | filters=512 709 | size=1 710 | stride=1 711 | pad=1 712 | activation=mish 713 | 714 | [convolutional] 715 | batch_normalize=1 716 | filters=512 717 | size=3 718 | stride=1 719 | pad=1 720 | activation=mish 721 | 722 | [shortcut] 723 | from=-3 724 | activation=linear 725 | 726 | [convolutional] 727 | batch_normalize=1 728 | filters=512 729 | size=1 730 | stride=1 731 | pad=1 732 | activation=mish 733 | 734 | [route] 735 | layers = -1,-16 736 | 737 | [convolutional] 738 | batch_normalize=1 739 | filters=1024 740 | size=1 741 | stride=1 742 | pad=1 743 | activation=mish 744 | 745 | ########################## 746 | 747 | [convolutional] 748 | batch_normalize=1 749 | filters=512 750 | size=1 751 | stride=1 752 | pad=1 753 | activation=leaky 754 | 755 | [convolutional] 756 | batch_normalize=1 757 | size=3 758 | stride=1 759 | pad=1 760 | filters=1024 761 | activation=leaky 762 | 763 | [convolutional] 764 | batch_normalize=1 765 | filters=512 766 | size=1 767 | stride=1 768 | pad=1 769 | activation=leaky 770 | 771 | ### SPP ### 772 | [maxpool] 773 | stride=1 774 | size=5 775 | 776 | [route] 777 | layers=-2 778 | 779 | [maxpool] 780 | stride=1 781 | size=9 782 | 783 | [route] 784 | layers=-4 785 | 786 | [maxpool] 787 | stride=1 788 | size=13 789 | 790 | [route] 791 | layers=-1,-3,-5,-6 792 | ### End SPP ### 793 | 794 | [convolutional] 795 | batch_normalize=1 796 | filters=512 797 | size=1 798 | stride=1 799 | pad=1 800 | activation=leaky 801 | 802 | [convolutional] 803 | batch_normalize=1 804 | size=3 805 | stride=1 806 | pad=1 807 | filters=1024 808 | activation=leaky 809 | 810 | [convolutional] 811 | batch_normalize=1 812 | filters=512 813 | size=1 814 | stride=1 815 | pad=1 816 | activation=leaky 817 | 818 | [convolutional] 819 | batch_normalize=1 820 | filters=256 821 | size=1 822 | stride=1 823 | pad=1 824 | activation=leaky 825 | 826 | [upsample] 827 | stride=2 828 | 829 | [route] 830 | layers = 85 831 | 832 | [convolutional] 833 | batch_normalize=1 834 | filters=256 835 | size=1 836 | stride=1 837 | pad=1 838 | activation=leaky 839 | 840 | [route] 841 | layers = -1, -3 842 | 843 | [convolutional] 844 | batch_normalize=1 845 | filters=256 846 | size=1 847 | stride=1 848 | pad=1 849 | activation=leaky 850 | 851 | [convolutional] 852 | batch_normalize=1 853 | size=3 854 | stride=1 855 | pad=1 856 | filters=512 857 | activation=leaky 858 | 859 | [convolutional] 860 | batch_normalize=1 861 | filters=256 862 | size=1 863 | stride=1 864 | pad=1 865 | activation=leaky 866 | 867 | [convolutional] 868 | batch_normalize=1 869 | size=3 870 | stride=1 871 | pad=1 872 | filters=512 873 | activation=leaky 874 | 875 | [convolutional] 876 | batch_normalize=1 877 | filters=256 878 | size=1 879 | stride=1 880 | pad=1 881 | activation=leaky 882 | 883 | [convolutional] 884 | batch_normalize=1 885 | filters=128 886 | size=1 887 | stride=1 888 | pad=1 889 | activation=leaky 890 | 891 | [upsample] 892 | stride=2 893 | 894 | [route] 895 | layers = 54 896 | 897 | [convolutional] 898 | batch_normalize=1 899 | filters=128 900 | size=1 901 | stride=1 902 | pad=1 903 | activation=leaky 904 | 905 | [route] 906 | layers = -1, -3 907 | 908 | [convolutional] 909 | batch_normalize=1 910 | filters=128 911 | size=1 912 | stride=1 913 | pad=1 914 | activation=leaky 915 | 916 | [convolutional] 917 | batch_normalize=1 918 | size=3 919 | stride=1 920 | pad=1 921 | filters=256 922 | activation=leaky 923 | 924 | [convolutional] 925 | batch_normalize=1 926 | filters=128 927 | size=1 928 | stride=1 929 | pad=1 930 | activation=leaky 931 | 932 | [convolutional] 933 | batch_normalize=1 934 | size=3 935 | stride=1 936 | pad=1 937 | filters=256 938 | activation=leaky 939 | 940 | [convolutional] 941 | batch_normalize=1 942 | filters=128 943 | size=1 944 | stride=1 945 | pad=1 946 | activation=leaky 947 | 948 | ########################## 949 | 950 | [convolutional] 951 | batch_normalize=1 952 | size=3 953 | stride=1 954 | pad=1 955 | filters=256 956 | activation=leaky 957 | 958 | [convolutional] 959 | size=1 960 | stride=1 961 | pad=1 962 | filters=255 963 | activation=linear 964 | 965 | 966 | [yolo] 967 | mask = 0,1,2 968 | anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 969 | classes=80 970 | num=9 971 | jitter=.3 972 | ignore_thresh = .7 973 | truth_thresh = 1 974 | scale_x_y = 1.2 975 | iou_thresh=0.213 976 | cls_normalizer=1.0 977 | iou_normalizer=0.07 978 | iou_loss=ciou 979 | nms_kind=greedynms 980 | beta_nms=0.6 981 | 982 | 983 | [route] 984 | layers = -4 985 | 986 | [convolutional] 987 | batch_normalize=1 988 | size=3 989 | stride=2 990 | pad=1 991 | filters=256 992 | activation=leaky 993 | 994 | [route] 995 | layers = -1, -16 996 | 997 | [convolutional] 998 | batch_normalize=1 999 | filters=256 1000 | size=1 1001 | stride=1 1002 | pad=1 1003 | activation=leaky 1004 | 1005 | [convolutional] 1006 | batch_normalize=1 1007 | size=3 1008 | stride=1 1009 | pad=1 1010 | filters=512 1011 | activation=leaky 1012 | 1013 | [convolutional] 1014 | batch_normalize=1 1015 | filters=256 1016 | size=1 1017 | stride=1 1018 | pad=1 1019 | activation=leaky 1020 | 1021 | [convolutional] 1022 | batch_normalize=1 1023 | size=3 1024 | stride=1 1025 | pad=1 1026 | filters=512 1027 | activation=leaky 1028 | 1029 | [convolutional] 1030 | batch_normalize=1 1031 | filters=256 1032 | size=1 1033 | stride=1 1034 | pad=1 1035 | activation=leaky 1036 | 1037 | [convolutional] 1038 | batch_normalize=1 1039 | size=3 1040 | stride=1 1041 | pad=1 1042 | filters=512 1043 | activation=leaky 1044 | 1045 | [convolutional] 1046 | size=1 1047 | stride=1 1048 | pad=1 1049 | filters=255 1050 | activation=linear 1051 | 1052 | 1053 | [yolo] 1054 | mask = 3,4,5 1055 | anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 1056 | classes=80 1057 | num=9 1058 | jitter=.3 1059 | ignore_thresh = .7 1060 | truth_thresh = 1 1061 | scale_x_y = 1.1 1062 | iou_thresh=0.213 1063 | cls_normalizer=1.0 1064 | iou_normalizer=0.07 1065 | iou_loss=ciou 1066 | nms_kind=greedynms 1067 | beta_nms=0.6 1068 | 1069 | 1070 | [route] 1071 | layers = -4 1072 | 1073 | [convolutional] 1074 | batch_normalize=1 1075 | size=3 1076 | stride=2 1077 | pad=1 1078 | filters=512 1079 | activation=leaky 1080 | 1081 | [route] 1082 | layers = -1, -37 1083 | 1084 | [convolutional] 1085 | batch_normalize=1 1086 | filters=512 1087 | size=1 1088 | stride=1 1089 | pad=1 1090 | activation=leaky 1091 | 1092 | [convolutional] 1093 | batch_normalize=1 1094 | size=3 1095 | stride=1 1096 | pad=1 1097 | filters=1024 1098 | activation=leaky 1099 | 1100 | [convolutional] 1101 | batch_normalize=1 1102 | filters=512 1103 | size=1 1104 | stride=1 1105 | pad=1 1106 | activation=leaky 1107 | 1108 | [convolutional] 1109 | batch_normalize=1 1110 | size=3 1111 | stride=1 1112 | pad=1 1113 | filters=1024 1114 | activation=leaky 1115 | 1116 | [convolutional] 1117 | batch_normalize=1 1118 | filters=512 1119 | size=1 1120 | stride=1 1121 | pad=1 1122 | activation=leaky 1123 | 1124 | [convolutional] 1125 | batch_normalize=1 1126 | size=3 1127 | stride=1 1128 | pad=1 1129 | filters=1024 1130 | activation=leaky 1131 | 1132 | [convolutional] 1133 | size=1 1134 | stride=1 1135 | pad=1 1136 | filters=255 1137 | activation=linear 1138 | 1139 | 1140 | [yolo] 1141 | mask = 6,7,8 1142 | anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 1143 | classes=80 1144 | num=9 1145 | jitter=.3 1146 | ignore_thresh = .7 1147 | truth_thresh = 1 1148 | random=1 1149 | scale_x_y = 1.05 1150 | iou_thresh=0.213 1151 | cls_normalizer=1.0 1152 | iou_normalizer=0.07 1153 | iou_loss=ciou 1154 | nms_kind=greedynms 1155 | beta_nms=0.6 1156 | 1157 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 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You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . --------------------------------------------------------------------------------